System and method for a usage category specific self-organizing network

ABSTRACT

A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to select a base station; obtain one or more metric values for user equipment (UE) devices attached to the selected base station; and determine usage categories for at least some of the UE devices attached to the selected base station, wherein a usage category identifies a combination of a data type, a movement type, and a user type associated with a particular UE device. The processor may be further configured to execute the instructions to classify the obtained one or more metric values based on the determined usage categories; select one or more optimization actions for the selected base station based on the classified one or more metric values; and instruct the selected base station to perform the selected one or more optimization actions.

BACKGROUND INFORMATION

In order to satisfy the needs and demands of users of mobilecommunication devices, providers of wireless communication servicescontinue to improve and expand available services as well as networksused to deliver such services. One aspect of such improvements includesthe development of wireless access networks as well as options toutilize such wireless access networks. The provider may manage a largenumber of wireless access networks and a particular wireless accessnetwork may manage and service a large number of devices. In order tomaintain a quality of service across a network, or across multiplenetworks, the provider may need to take into account various conditionsthat vary across networks and/or devices.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an environment according to animplementation described herein;

FIG. 2 is a diagram illustrating exemplary components of a device thatmay be included in a device or system of FIG. 1;

FIG. 3 is a diagram illustrating exemplary functional components of theself-optimizing network system of FIG. 1;

FIG. 4 is a diagram illustrating exemplary functional components of themetrics system of FIG. 1;

FIG. 5 is a diagram illustrating exemplary functional components of abase station of FIG. 1;

FIG. 6A is a diagram illustrating exemplary components of theoptimization database of FIG. 3;

FIG. 6B is a diagram illustrating exemplary components of the metricsdatabase of FIG. 4;

FIG. 6C is a diagram illustrating exemplary components of theclassification database of FIG. 4;

FIG. 7 is a flowchart of a process for configuring a self-optimizingnetwork based on usage categories according to an implementationdescribed herein;

FIG. 8 is a flowchart of a process for self-optimization based on usagecategories according to an implementation described herein;

FIG. 9 is a diagram of an exemplary set of behavior patterns fordifferent usage categories according to an implementation describedherein; and

FIG. 10 is a diagram of an exemplary set of optimization actions fordifferent usage categories according to an implementation describedherein.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings identify the same orsimilar elements.

A wireless access network may enable wireless communication devices toconnect to a base station via wireless signals. A wireless communicationdevice may connect to a provider network via the wireless access networkand may use various applications, such as voice communication, videostreaming, sending or receiving data via the Internet, etc. Furthermore,wireless communication devices may be mobile and may move out ofcommunication range of a first base station and into the communicationrange of a second base station. In response, the wireless access networkmay handover control and service of a wireless communication device fromthe first base station to the second base station.

In the course of providing services to wireless communication devices,the operating conditions of the wireless access network may change. Asan example, the number of wireless devices attached to a base stationmay increase and reduce the available capacity of the base station. Asanother example, a base station may experience fading of wirelesssignals on particular channels due to changes in the environment. As yetanother example, a list of neighboring base stations, referred to as a“neighbor list,” may change as base stations are added, removed, orchanged. In the past, wireless networks had to be optimized manually inresponse to such changes.

Self-organizing networks (SONs) have enabled automation of optimizationfunctions for wireless networks and may be deployed at a scale to managewireless networks, such as 4G and 5G wireless networks. SON functionsmay be used to enable discovery and optimization of base stationneighbor lists, modification of antenna tilts or directions to improvecoverage or capacity, changes to handoff parameters to reduce handoverdrops, and/or other types of parameters that previously requiredlaborious procedures to be executed manually.

SON functions may be carried out by obtaining various metrics, alsoreferred to as key performance indicators (KPIs), across a large numberof base stations and user equipment (UE) devices and to performautonomous analysis on the obtained metrics. The result of the analysismay indicate a change in one or more parameters of a base station tooptimize (i.e., improve) the functioning of the base station in responseto changing conditions.

As the data traffic and number of UE devices using wireless accessnetworks increase, the number of different types of UE devices and thenumber of different types of data also increase. As an example, anexponential growth in Internet of Things (IoT) applications leads to anincreasing number of different types of UE devices employingmachine-to-machine (M2M) communication, such as Machine-TypeCommunication (MTC), a type of M2M communication standard developed bythe 3^(rd) Generation Partnership Project (3GPP). As another example,wireless communication is increasingly being used for new types ofapplications, such as connected cars, connected public transportsystems, and drones. New types of UE devices and/or new types of usecases for wireless communication may challenge existing SON processes.

As an example, a large number of MTC drones, or a large number ofstationary MTC devices, such as parking meters, may provide largeamounts of data that could influence the operation of a SON optimizationprocess and result in actions that skew the results and are notappropriate for other use cases, such as pedestrian mobile phonecoverage, mobile vehicular traffic, or wireless coverage inside abuilding. In situations where the number of MTC devices exceeds mobilebroadband user devices, SON algorithms may be steered in the wrongdirection by being skewed toward optimizing performance of MTC devicesto the detriment of mobile broadband user devices. As another example,airborne unmanned vehicles, such as drones, may cause antennaadjustments that are not appropriate for ground-based devices.

Implementations described herein relate to a usage category specificSONs. A usage category may be defined as a particular combination of adata type, a movement type, and a user type. A data type may correspondto a type of data being sent or received by a UE device, such as, forexample, voice communication data traffic type, real-time videostreaming data traffic type, real-time gaming data traffic type,buffered streaming video data traffic type, mobile broadband datatraffic type, critical (e.g., emergency medical data) data traffic type,priority data traffic type, best effort data traffic type, and/oranother type of data traffic. Furthermore, the data type may identify adata rate (e.g., a low data rate, a medium rate data rate, a high datarate, etc.) specified by a particular data rate sent by a UE devicewithin a particular time period.

Movement type may correspond to a type of movement associated with theUE device. For example, UE device may be stationary, may move at apedestrian or walking speed, may move at a vehicular speed, may beairborne, and/or may exhibit a different type of movement (e.g., aparticular type of vehicular movement, such as a particular speed range,stop-and-go vs. continuous movement, etc.). A user type may correspondto a type of user class associated with the UE device. For example, UEdevice may correspond to an MTC device (i.e., no human user), maycorrespond to a single human user (e.g., a mobile phone, wearablecomputer device, etc.), may correspond to a multiple human users (e.g.,a WiFi access point connected to the Internet using a wireless accessnetwork), and/or may correspond to a different type of user class (e.g.,a number of human users within a particular range, such as one to twentyusers, etc.).

For example, the usage category may corresponds to a voice usagecategory, a mobile broadband usage category, a mobile high speed usagecategory, a connected mass transit usage category, an airborne MTC usagecategory, a video feed MTC usage category, a stationary low data MTCdevice category, a best effort usage category, and/or another type ofusage category.

Implementations described herein relate to identifying usage categoriesfor UE devices, separating collection of metrics based on usagecategories, and enabling different SON triggers and/or actions fordifferent usage categories. A computer device may select a base station,obtain one or more metric values for UE devices attached to the selectedbase station, and determine usage categories for the UE devices attachedto the selected base station. The computer device may then classify theobtained one or more metric values based on the determined usagecategories, select one or more optimization actions for the selectedbase station based on the classified one or more metric values, andinstruct the selected base station to perform the selected one or moreoptimization actions.

The usage categories for the UE devices may be determined by selecting aUE device attached to the selected base station, obtaining a deviceidentifier (ID) for the selected UE device, and identifying a usagecategory for the selected UE device based on the obtained device ID.Additionally or alternatively, the usage categories for UE devices maybe identified by determining one or more behavior patterns for theselected UE device. The one or more behavior patterns may include atleast one of a performance metric value, a connectivity metric value, aservice type metric value, a location or movement metric value, and/oranother type of metric value.

The metric values may include, for example, an elevation of the UEdevice, a maximum speed of the UE device, a number of voice bearersassociated with the UE device, a number of video bearers associated withthe UE device, a number of users associated with the UE device, a datathroughput for the UE device, a number of handovers for the UE device, apacket size variability for the UE device, a variance in packet arrivaltimes for the UE device, a connection success rate for the UE device, acall drop rate for the UE device, a latency for the UE device, a numberof unique cells reported in a time period, a number of cells changed ina time period, an error rate for the UE device, and/or another type ofmetric value associated with the UE device.

The one or more optimization actions may include adjusting at least oneof a coverage optimization parameter, a capacity optimization parameter,a handover parameter, a neighbor list changes parameter, an antenna tiltparameter, a delay optimization parameter, a carrier optimizationparameter, a random access channel parameter, and/or another type ofoptimization parameter.

Furthermore selecting the one or more optimization actions may includeidentifying a restriction associated with at least one of the determinedusage categories that designates one or more parameters that are not tobe adjusted and selecting not to perform an optimization action for thedesignated one or more parameters.

Configuring optimizations actions for particular usage categories mayinclude selecting a usage category, selecting an optimization actionassociated with the usage category, and setting an optimizationthreshold for a metric value associated with the selected optimizationaction. When the set optimization threshold for the metric value isexceeded, the optimization action may be performed with respect to theselected usage category.

As an example, determining the usage category for a UE device mayinclude determining an airborne MTC usage category based on at least oneof a number of measurement reports within a first time period for the UEdevice exceeding a first threshold, a number of unique base stationcells identified in a measurement report for the UE device exceeding asecond threshold, a number of cell changes in a time period, a maximumelevation for the UE device exceeding a third threshold, or a differencebetween maximum and minimum elevation within a second time period forthe UE device exceeding a fourth threshold; and selecting the one ormore optimization actions based on the determined airborne MTC usagecategory may include at least one of optimizing an auxiliary tilt for anantenna associated with the selected base station, or optimizing asecond component carrier associated with the selected base station.

As another example determining the usage category for a UE device mayinclude determining a stationary low data rate MTC usage category basedon at least one of a number of handovers within a time period for the UEdevice is zero, a maximum distance change within the time period for theUE device is less than a first threshold, or a maximum number of bytessent or received by the UE device per attachment is less than a secondthreshold; and selecting the one or more optimization actions based onthe determined stationary low data rate MTC usage category may includeat least one of optimizing a physical random access channel associatedwith the selected base station or optimizing a communication delayassociated with the selected base station.

As yet another example, determining the usage category for a UE devicemay include determining a video feed MTC usage category based on atleast one of a number of video bearers set up within a time period forthe UE device is zero, a packet size variability within the time periodfor the UE device is less than a first threshold, or a variance inpacket arrival times within the time period for the UE device is lessthan a second threshold; and selecting the one or more optimizationactions based on the determined video feed MTC usage category, whereinthe one or more optimization actions include performing a capacityoptimization for the selected base station.

As yet another example, determining the usage category for a UE devicemay include determining a connected mass transit usage category for a UEdevice based on determining that the UE device is associated with anumber of users that is greater than a user threshold; and selecting theone or more optimization actions based on the determined connected masstransit category may include at least one of optimizing handoverparameters associated with the selected base station or performing acapacity optimization for the selected base station.

In some implementations, the computer device may be included in acentralized SON system that manages a provider's wireless accessnetwork. In other implementations, some or all of the functionality ofthe computer device may be distributed. For example, SON functionsrelating to handover optimization may be performed locally by a basestation or by a group of base stations, whereas SON functions relatingto large parts of a network (e.g., coverage optimization) may beperformed by a centralized SON system.

FIG. 1 is a diagram of an exemplary environment 100 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.1, environment 100 may include UE device 110, an access network 120, andprovider network 140. Access network 120 may include a base station 130and UE device 110 may connect to access network 120 via base station 130using wireless signals. While a single UE device 110, a single basestation 130, and a single access network 120 are shown in FIG. 1 forillustrative purposes, in practice, environment 100 may include multipleUE devices 110, multiple base stations 130, and multiple access networks120. For example, multiple UE devices 110 may be attached to basestation 130 and access network 120 may include multiple base stations130. Furthermore, a provider of communication services may managemultiple access networks 120 connected to provider network 140.

In some implementations, UE device 110 may include a handheld wirelesscommunication device (e.g., a mobile phone, a smart phone, a phabletdevice, etc.); a wearable computer device (e.g., a head-mounted displaycomputer device, a head-mounted camera device, a wristwatch computerdevice, etc.), a global positioning system (GPS) device; a laptopcomputer, a tablet computer, or another type of portable computer; amedia playing device; a portable gaming system; a home appliance device;a home monitoring device; and/or any other type of computer device withwireless communication capabilities. UE device 110 may be used for voicecommunication, mobile broadband services (e.g., video streaming,real-time gaming, premium Internet access etc.), best effort datatraffic, and/or other types of applications. Furthermore, UE device 110may be stationary, moving at a pedestrian speed, moving at a vehicularspeed, and/or moving at a different rate of speed.

In other implementations, UE device 110 may correspond to an embeddedwireless device that communicates wirelessly with other devices over anM2M interface using MTC and/or another type of M2M communication. As anexample, UE device 110 may be electrically connected or coupled to asensor device, an actuator device, a microcontroller controlling one ormore sensors, a microcontroller controlling one or more actuators, amicrocontroller that performs data processing, and/or another type ofMTC device. Examples of such devices may include a health monitoringdevice (e.g., a blood pressure monitoring device, a blood glucosemonitoring device, etc.), an asset tracking device (e.g., a systemmonitoring the geographic location of a fleet of vehicles, etc.), atraffic management device (e.g., a traffic light, traffic camera, roadsensor, road illumination light, etc.), a device controlling one or morefunctions of a vehicle (e.g., a climate control system, an enginemonitoring system, etc.), a device controlling an electronic sign (e.g.,an electronic billboard, etc.), a device controlling a manufacturingsystem (e.g., a robot arm, an assembly line, etc.), a device controllinga security system (e.g., a camera, a motion sensor, a window sensor,etc.), a device controlling a power system (e.g., a smart gridmonitoring device, a utility meter, a fault diagnostics device, etc.), adevice controlling a financial transaction system (e.g., a point-of-saleterminal, a vending machine, a parking meter, etc.), and/or another typeof electronic device.

An MTC device may correspond to a stationary low data rate MTC device(e.g., parking meter), a stationary high data rate MTC device (e.g., acamera providing a video feed), an MTC device moving at pedestrianspeeds (e.g., a health monitoring device attached to a user), and MTCdevice moving at vehicular speed (e.g., a vehicle telematics device),and/or an MTC device associated with other types of data rates and/ormovement.

As yet another example, UE device 110 may correspond to an unmannedaerial vehicle or an unmanned aircraft system that communicateswirelessly with other devices over an M2M interface using MTC and/oranother type of M2M communication. Examples of such airborne MTC devicesinclude consumer drone devices used for entertainment, photo or videocapture, payload delivery, and/or other uses; commercial delivery dronesused to deliver packages to customers; law enforcement drones used forintelligence gathering operations; and/or other types of drones oraerial devices.

In yet other implementations, UE device 110 may correspond to an accesspoint servicing multiple devices associated with multiple users. As anexample, UE device 110 may include a stationary WiFi access pointlocated in a customer premises location or a business location. Asanother example, UE device 110 may include a WiFi access point thatexperiences movement, such as a WiFi access point located in a masstransit vehicle (e.g., a bus, a train, a trolley, a ferry, an airplane,etc.). The WiFi access point may communicate with base station 130 viawireless signals.

Access network 120 may provide access to provider network 140 forwireless devices, such as UE device 110. Access network 120 may providemobile telephone service and/or data services to UE device 110. Forexample, access network 120 may establish a packet data networkconnection (e.g., an Internet Protocol (IP) connection) between UEdevice 110 and provider network 140. In some implementations, accessnetwork 120 may include a Long Term Evolution (LTE) access network(e.g., an evolved packet core (EPC) network) based on the LTE standardspecified by the 3^(rd) Generation Partnership Project (3GPP). In otherimplementations, access network 120 may include an LTE Advanced (LTE-A)access network and/or a 5G access network that includes functionalitysuch as carrier aggregation; advanced or massive multiple-input andmultiple-output (MIMO) configurations (e.g., an 8×8 antennaconfiguration, a 16×16 antenna configuration, a 256×256 antennaconfiguration, etc.); cooperative MIMO (CoMP); relay stations;Heterogeneous Networks (HetNets) of overlapping small cells andmacrocells; Self-Organizing Network (SON) functionality; MTCfunctionality, such as 1.4 MHz wide enhanced MTC (eMTC) channels (alsoreferred to as category Cat-M1), Low Power Wide Area (LPWA) technologysuch as Narrow Band (NB) IoT (NB-IoT) technology, and/or other types ofMTC technology; and/or other types of LTE-A and/or 5G functionality. Inyet other implementations, access network 120 may include a CodeDivision Multiple Access (CDMA) access network based on, for example, aCDMA1000 standard. For example, the CDMA access network may include aCDMA enhanced High Rate Packet Data (eHRPD) network (which may provideaccess to an LTE access network).

As stated above, access network 120 may include a base station 130 andUE device 110 may wirelessly communicate with access network 120 viabase station 130. In other words, UE device 110 may be located withinthe geographic area serviced by base station 130. Base station 130 maybe part of an LTE eNodeB base station device and/or another LTE-A or 5Gbase station device. An eNodeB base station device may use the EvolvedUniversal Terrestrial Radio Access (E-UTRA) air interface or other airinterfaces to wirelessly communicate with devices. An eNodeB basestation device may include one or more devices (e.g., base stations 130)and other components and functionality that allow UE device 110 towirelessly connect to access network 120. The eNodeB base station devicemay include or be associated with one or more cells. For example, eachcell may include an RF transceiver facing a particular direction. TheeNodeB base station device may correspond to a macrocell or to a smallcell (e.g., a femtocell, a picocell, a microcell, etc.).

Provider network 140 may be managed, at least in part, by a provider ofcommunication services associated with access network 120. Providernetwork 140 may include a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), an optical network, a cabletelevision network, a satellite network, a wireless network (e.g., aCode Division Multiple Access (CDMA) network, a general packet radioservice (GPRS) network, and/or an LTE network), an ad hoc network, atelephone network (e.g., the Public Switched Telephone Network (PSTN) ora cellular network), an intranet, the Internet, or a combination ofnetworks. Provider network 140 may allow the delivery of InternetProtocol (IP) services to UE device 110, and may interface with otherexternal networks. In some implementations, provider network 140 mayinclude an Internet Protocol Multimedia Subsystem (IMS) network (notshown in FIG. 1). An IMS network may include a network for delivering IPmultimedia services as specified by 3GPP or other standards/protocolsand may provide media flows between UE device 110 and external IPnetworks or external circuit-switched networks (not shown in FIG. 1).Provider network 140 may include one or more server devices and/ornetwork devices, or other types of computation or communication devicesto manage the functionality of provider network 140. For example,provider network 140 may include a self-optimizing network (SON) system150 and a metric system 160.

SON system 150 may include one or more devices, such as computer devicesand/or server devices, which perform self-optimization functions forprovider network 140. In particular, SON system 150 may optimize one ormore parameters for base station 130 based on usage categoriesassociated with UE devices 110 attached to base station 130.

Metrics system 160 may include one or more devices, such as computerdevices and/or server devices, which collect metric values for metricsassociated with UE devices 110. For example, metrics system 160 mayreceive, at particular intervals, metric values associated with UEdevices 110 attached to base station 130. Metrics system 160 mayclassify UE devices 110 into usage categories and may classify metricvalues associated with particular UE devices 110 into usage categoriesassociated with the particular UE devices 110. Metrics system 160 mayprovide information relating to metrics classified into usage categoriesto SON system 150.

Although FIG. 1 shows exemplary components of environment 100, in otherimplementations, environment 100 may include fewer components, differentcomponents, differently arranged components, or additional functionalcomponents than depicted in FIG. 1. Additionally or alternatively, oneor more components of environment 100 may perform functions described asbeing performed by one or more other components of environment 100. Forexample, in some implementations, some or all of the functionality ofmetrics system 160 may be implemented in SON system 150. Additionally oralternatively, some or all of the functionality of SON system 150 may beimplemented by base station 130 or by a group of base stations 130.

FIG. 2 is a diagram illustrating exemplary components of device 200according to an implementation described herein. SON system 150, metricssystem 160, base station 130, and/or UE device 110 may each include oneor more devices 200. As shown in FIG. 2, device 200 may include a bus210, a processor 220, a memory 230, an input device 240, an outputdevice 250, and a communication interface 260.

Bus 210 may include a path that permits communication among thecomponents of device 200. Processor 220 may include any type ofsingle-core processor, multi-core processor, microprocessor, latch-basedprocessor, and/or processing logic (or families of processors,microprocessors, and/or processing logics) that interprets and executesinstructions. In other embodiments, processor 220 may include anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and/or another type of integrated circuit orprocessing logic.

Memory 230 may include any type of dynamic storage device that may storeinformation and/or instructions, for execution by processor 220, and/orany type of non-volatile storage device that may store information foruse by processor 220. For example, memory 230 may include a randomaccess memory (RAM) or another type of dynamic storage device, aread-only memory (ROM) device or another type of static storage device,a content addressable memory (CAM), a magnetic and/or optical recordingmemory device and its corresponding drive (e.g., a hard disk drive,optical drive, etc.), and/or a removable form of memory, such as a flashmemory.

Input device 240 may allow an operator to input information into device200. Input device 240 may include, for example, a keyboard, a mouse, apen, a microphone, a remote control, an audio capture device, an imageand/or video capture device, a touch-screen display, and/or another typeof input device. In some embodiments, device 200 may be managed remotelyand may not include input device 240. In other words, device 200 may be“headless” and may not include a keyboard, for example.

Output device 250 may output information to an operator of device 200.Output device 250 may include a display, a printer, a speaker, and/oranother type of output device. For example, device 200 may include adisplay, which may include a liquid-crystal display (LCD) for displayingcontent to the customer. In some embodiments, device 200 may be managedremotely and may not include output device 250. In other words, device200 may be “headless” and may not include a display, for example.

Communication interface 260 may include a transceiver that enablesdevice 200 to communicate with other devices and/or systems via wirelesscommunications (e.g., radio frequency, infrared, and/or visual optics,etc.), wired communications (e.g., conductive wire, twisted pair cable,coaxial cable, transmission line, fiber optic cable, and/or waveguide,etc.), or a combination of wireless and wired communications.Communication interface 260 may include a transmitter that convertsbaseband signals to radio frequency (RF) signals and/or a receiver thatconverts RF signals to baseband signals. Communication interface 260 maybe coupled to an antenna for transmitting and receiving RF signals. Ifdevice 200 is included in UE device 110 or base station 130,communication interface 260 may include one or more antenna assemblies.For example, each cell associated with base station 130 may include anRF transceiver and a tunable antenna assembly.

Communication interface 260 may include a logical component thatincludes input and/or output ports, input and/or output systems, and/orother input and output components that facilitate the transmission ofdata to other devices. For example, communication interface 260 mayinclude a network interface card (e.g., Ethernet card) for wiredcommunications and/or a wireless network interface (e.g., a WiFi) cardfor wireless communications. Communication interface 260 may alsoinclude a universal serial bus (USB) port for communications over acable, a Bluetooth™ wireless interface, a radio-frequency identification(RFID) interface, a near-field communications (NFC) wireless interface,and/or any other type of interface that converts data from one form toanother form.

As will be described in detail below, device 200 may perform certainoperations relating to optimization based on usage categories. Device200 may perform these operations in response to processor 220 executingsoftware instructions contained in a computer-readable medium, such asmemory 230. A computer-readable medium may be defined as anon-transitory memory device. A memory device may be implemented withina single physical memory device or spread across multiple physicalmemory devices. The software instructions may be read into memory 230from another computer-readable medium or from another device. Thesoftware instructions contained in memory 230 may cause processor 220 toperform processes described herein. Alternatively, hardwired circuitrymay be used in place of, or in combination with, software instructionsto implement processes described herein. Thus, implementations describedherein are not limited to any specific combination of hardware circuitryand software.

Although FIG. 2 shows exemplary components of device 200, in otherimplementations, device 200 may include fewer components, differentcomponents, additional components, or differently arranged componentsthan depicted in FIG. 2. Additionally or alternatively, one or morecomponents of device 200 may perform one or more tasks described asbeing performed by one or more other components of device 200.

FIG. 3 is a diagram illustrating exemplary functional components of SONsystem 150. The functional components of SON system 150 may beimplemented, for example, via processor 220 executing instructions frommemory 230. Alternatively, some or all of the functional componentsincluded in SON system 150 may be implemented via hard-wired circuitry.As shown in FIG. 3, SON system 150 may include a metrics systeminterface 310, an optimization manager 320, an optimization database(DB) 330, a base station manager 340, and a base station DB 350.

Metrics system interface 310 may be configured to communicate withmetrics system 160. For example, metrics system interface 310 may obtainthe classified metrics information from metrics DB 420 of metrics system160 and provide the classified metrics information to optimizationmanager 320. Optimization manager 320 may perform SON actions for basestations 130 based on metric values classified based on usagecategories. Optimization manager 320 may obtained classified metricsinformation from metrics system 160, and/or from base stations 130,associated with a particular base station 130, and may select one ormore optimization actions for the particular base station 130 based oninformation stored in optimization DB 330. Optimization DB 330 may storeinformation relating to optimization actions associated with particularusage categories. Exemplary information that may be stored inoptimization DB 330 is described below with reference to FIG. 6A.

Base station manager 340 may instruct a base station 130 to adjust oneor more optimization parameters based on selections made by optimizationmanager 320. Base station DB 350 may store information identifyingparticular base stations 130 associated with access networks 120. Forexample, base station DB 350 may identify base stations 130 (e.g.,eNodeBs) with which SON system 150 is configured to communicate and maystore information on how to reach the eNodeBs and/or how to instruct theeNodeBs to adjust the optimization parameters. Furthermore, base stationDB 350 may store information relating to current optimization settingsassociated with particular base stations 130.

Although FIG. 3 shows exemplary components of SON system 150, in otherimplementations, SON system 150 may include fewer components, differentcomponents, additional components, or differently arranged componentsthan depicted in FIG. 3. Additionally or alternatively, one or morecomponents of SON system 150 may perform one or more tasks described asbeing performed by one or more other components of SON system 150.Furthermore, in some implementations, some or all of the functionalcomponents of SON system 150 shown in FIG. 3 may be implemented in oneor more devices of system 100, such as, for example, base station 130.

FIG. 4 is a diagram illustrating exemplary functional components ofmetrics system 160. The functional components of metrics system 160 maybe implemented, for example, via processor 220 executing instructionsfrom memory 230. Alternatively, some or all of the functional componentsincluded in metrics system 160 may be implemented via hard-wiredcircuitry. As shown in FIG. 4, metrics system 160 may include a metricscollector 410, a metrics DB 420, a usage classifier 430, a UE device DB440, a classification DB 450, and a SON system interface 460.

Metrics collector 410 may collect metric values from base stations 130.As an example, base station 130 may collect metric values for UE devices110 attached to base station 130 and may send the collected metricvalues to metrics collector 410 at particular intervals. As anotherexample, metrics collector 410 may poll base stations 130 for thecollected metric values at particular intervals. As yet another example,metrics collector 410 may intercept data traffic sent by base station130 via a backhaul link to access network 120 and may collect metricvalues from the intercepted data traffic. Additionally or alternatively,metric collector 410 may obtain metric values for UE devices 110attached to base station 130 from other devices in access network 120,such as a Mobility Management Entity (MME), a Packet Data NetworkGateway (PGW), and/or other types of devices. Metrics DB 420 may storemetrics values obtained for particular base stations 130. Exemplaryinformation that may be stored in metrics DB 420 is described below withreference to FIG. 6B.

Usage classifier 430 may classify obtained metric values into usagecategories. As an example, usage classifier 430 may classify metricvalues based on device or application IDs, associated with metric valuesobtained from base stations 130 and stored in metrics DB 420, based oninformation stored in UE device DB 440. UE device DB 440 may storeinformation relating to particular UE devices. In some implementations,UE device DB 440 may store information that relates particular types ofUE device IDs to usage categories. For example, a particular device IDthat identifies a parking meter type may be associated with a stationarylow data rate MTC device usage category. Additionally or alternatively,UE device DB 440 may store information identifying particular UE devicesand relating the particular UE devices to usage categories. In someimplementations, usage classifier 430 may provide a reportingapplication to UE devices 110 that enables UE devices 110 to provideparticular device metrics to metrics system 160.

Furthermore, usage classifier 430 classify metric values, obtained frombase stations 130 and stored in metrics DB 420, based on informationstored in classification DB 450. Classification DB 450 may storeinformation relating usage categories to behavior patterns. Exemplaryinformation that may be stored in classification DB 450 is describedbelow with reference to FIG. 6C. SON system interface 460 may beconfigured to communicate with SON system 150. For example, SON systeminterface 460 may provide classified metric values, associated with aparticular base station 130 and stored in metrics DB 420, to SON system150.

Although FIG. 4 shows exemplary components of metrics system 160, inother implementations, metrics system 160 may include fewer components,different components, additional components, or differently arrangedcomponents than depicted in FIG. 4. Additionally or alternatively, oneor more components of metrics system 160 may perform one or more tasksdescribed as being performed by one or more other components of metricssystem 160.

FIG. 5 is a diagram illustrating exemplary functional components of basestation 130. The functional components of base station 130 may beimplemented, for example, via processor 220 executing instructions frommemory 230. Alternatively, some or all of the functional componentsincluded in base station 130 may be implemented via hard-wiredcircuitry. As shown in FIG. 5, base station 130 may include a metricssystem interface 510, a SON system interface 520, a base stationinterface 530, a metrics collector 540, an optimization manager 550, anoptimization DB 560, a handover manager 570, an antenna manager 580, anda load balancer 590.

Metrics system interface 510 may be configured to provide particularmetric values, associated with UE devices 110 attached to base station130, to metrics system 160. SON system interface 520 may be configuredto receive instructions from SON system 150 relating to SON parametersto be adjusted based on optimizations selected by SON system 150. Insome implementations, some SON adjustments may be selected by basestation 130 (e.g., local parameters such as handover parameters, antennatilt or direction adjustments, neighbor lists, etc.), other SONadjustments may be selected by SON system 150 (e.g., global parameters,such as coverage optimization, capacity optimization, etc.), and SONsystem interface 520 may communicate with SON system 150 to determinewhich parameter adjustments should be determined locally by optimizationmanager 550 and which parameters adjustments should be determined by SONsystem 150.

Base station interface 530 may be configured to communicate with otherbase stations 130. For example, some SON parameter adjustments may bebased on metrics associated with a set of base stations 130 in aparticular area, where base stations 130 in the area may communicate todetermine such SON parameter adjustments using distributed computing.For example, base stations 130 may share neighbor lists, coordinateantenna tilts to reduce interference, and/or load balance traffic byhanding over particular types of UE devices 110 to a particular basestation 130 with a higher capacity.

Metrics collector 540 may collect metric values associated with UEdevices 110 attached to base station 130. For example, metrics collector540 may collect, for a UE device 110 and for a particular time interval,information relating to location and elevation of UE device 110, averageand/or maximum speed of UE device 110, a number of voice bearersassociated with UE device 110, a number of video bearers associated withUE device 110, a number of users associated with UE device 110, a datathroughput for UE device 110,a number of handovers for UE device 110, apacket size variability for UE device 110, a variance in packet arrivaltimes for UE device 110, a connection success rate for UE device 110, acall drop rate for UE device 110, a latency for UE device 110, a numberof unique cells reported in a time period for UE device 110, a number ofcells changed in a time period for UE device 110, an error rate for UEdevice 110, and/or other metric values.

Optimization manager 550 may adjust SON optimization parameters for basestation 130. For example, optimization manager 550 may adjust one ormore of a coverage optimization parameter, a capacity optimizationparameter, a handover parameter, a neighbor list changes parameter, anantenna tilt or direction parameter, a delay optimization parameter, acarrier optimization parameter, a random access channel parameter,and/or another type of SON parameter. In some implementations,optimization manager 550 may receive instructions from SON system 150via SON system interface 520. For example, SON system interface 520 mayuse an API to instruct optimization manager 550 to adjust one or moreSON parameters. An API call by SON system interface 520 to optimizationmanager 550 may include, for each SON parameter to be adjusted, a SONparameter ID and a corresponding SON parameter adjustment value.

Additionally or alternatively, optimization manager 550 may determine toadjust one or more SON parameters locally based on information obtainedfrom metrics collector 540 and/or based on information stored inoptimization DB 560. Optimization DB 560 may store information relatingto optimization actions associated with particular usage categories,similar to the information stored in optimization DB 330 and describedbelow with reference to FIG. 6A. Optimization manager 550 may adjust theSON parameters for base station 130, including instructing one or moreof handover manager 570, antenna manager 580, and/or load balancer 590to perform operational parameters associated with the SON optimizationdeterminations.

Handover manager 570 may manage handovers of UE devices 110 from basestation 130 to another base station 130 and/or handovers received fromother base stations 130. Handover manager 570 may maintain a set ofhandover parameters (e.g., event A3 parameters, such as a3offset,Hysteresisa3, timetoTriggera3, CellIndividualoffsetEutran, etc.) thatare used to determine whether a particular UE device 110 should behanded over to another base station 130. Furthermore, handover manager570 may maintain a list of neighbors identifying neighboring basestations 130.

Antenna manager 580 may manage one or more antennas associated with oneor more cells of base station 130. Antenna manager 580 may performadjustments to an antenna radiation pattern for a particular frequencyband, such as, for example, mechanical tilt, remote electric tilt,auxiliary tilt, etc. Load balancer 590 may perform load balancing oftraffic for base station 130. Load balancing may be performed betweenparticular bands of base station 130, between particular cells of basestation 130, and/or may include coordination of load balancing oftraffic with other base stations 130.

Although FIG. 5 shows exemplary components of base station 130, in otherimplementations, base station 130 may include fewer components,different components, additional components, or differently arrangedcomponents than depicted in FIG. 5. Additionally or alternatively, oneor more components of base station 130 may perform one or more tasksdescribed as being performed by one or more other components of basestation 130.

FIG. 6A is a diagram illustrating exemplary components of optimizationDB 330. As shown in FIG. 6A, optimization DB 330 may include one or moreusage category records 610. A usage category record 610 may storeinformation relating to optimization associated with a particular usagecategory. Usage category record 610 may include a usage categoryidentifier (ID) field 612, one or more action fields 620, and one ormore restriction fields 630.

Usage category ID field 612 may include an identifier associated with aparticular usage category. In some implementations, the usage categorymay be identified by a combination of identifiers. For example, a firstidentifier may identify a data type associated with the particular usagecategory, a second identifier may identify a movement type associatedwith the particular usage category, and a third identifier may identifya user type associated with the particular usage category. It is to beunderstood that the number of potential usage categories is not limitedand may be revised as use cases change.

Each action field 620 may identify a particular optimization actionassociated with the particular usage category. For example, if theparticular usage category is associated with base station 130, theoptimization actions identified in action fields 620 for the particularusage category may be permitted and may be selected to be performed.Each action field 620 may include a particular optimization action and atriggering event for executing the optimization action. The triggeringevent may correspond to a particular threshold associated with aparticular metric. As an example, an optimization action to performcoverage optimization may include a triggering event (e.g., one or morethresholds are satisfied) that specifies that coverage optimization isto be triggered if a particular number of UE devices 110 experience aparticular number of dropped calls within a particular period of time.As another example, an optimization action to perform an adjustment ofhandover parameters may be triggered if a particular number of handoverevents is detected within a particular time period.

Each restriction field 630 may identify a particular optimization actionthat is restricted from being used in connection with a particular usagecategory. For example, if the particular usage category is associatedwith base station 130, the optimization actions identified inrestriction fields 630 for the particular usage category may beprevented from being carried out. Each restriction field 630 may includea triggering event (e.g., a threshold) that may trigger the restrictionto prevent the specified optimization action from being carried out.

Although FIG. 6A shows exemplary components of optimization DB 330, inother implementations, optimization DB 330 may include fewer components,different components, additional components, or differently arrangedcomponents than depicted in FIG. 6A.

FIG. 6B is a diagram illustrating exemplary components of metrics DB420. As shown in FIG. 6B, metrics DB 420 may include one or more basestation records 640. Each base station record 640 may store metricvalues associated with a particular base station 130. Base stationrecord 640 may include a base station ID field 642 one or more timeperiod records 650. Base station ID field 642 may include an identifierassociated with the particular base station. Each time period record 650may store metric value information for a particular time period. As anexample, time period record 650 may store metric values information fora repeating time period, such as a particular time of day, a particularday of the week, etc. As another example, time period record 650 maystore metric values information for a specific time period that occurredin the past, such as for particular time and day, for the past 24 hours,for the past hour, etc. As yet another example, a particular time periodrecord 650 may store the most recently available metric valuesinformation. Time period record 650 may include a time period field 652and one or more usage category records 660. Time period field 652 mayidentify the particular time period.

Each usage category record 660 may store metric values information for aparticular usage category associated with base station 130. Usagecategory record 660 may store a usage category ID field 662 and one ormore UE device records 670. Each UE device record 670 may store metricvalues for a particular UE device 110 attached to base station 130.

UE device record 670 may include a UE device field 672 and a metricvalues field 674. UE device field 672 may include informationidentifying a particular UE device 110 attached to base station 130. UEdevice 110 may be identified by an associated device identifier, such asa Media Access Control (MAC) address, an IP address, a SessionInitiation Protocol (SIP) address; an associated subscriber telephonenumber, such as a Mobile Station International Subscriber DirectoryNumber (MSISDN), an International Mobile Subscriber Identity (IMSI)number, a Mobile Directory Number (MDN); and/or by another type ofdevice identifier. Metric values field 674 may store one or more metricvalues determined for UE device 110, such as metric values describedabove with reference to metrics collector 540.

Although FIG. 6B shows exemplary components of metrics DB 420, in otherimplementations, metrics DB 420 may include fewer components, differentcomponents, additional components, or differently arranged componentsthan depicted in FIG. 6B.

FIG. 6C is a diagram illustrating exemplary components of classificationDB 450. As shown in FIG. 6C, classification DB 450 may include one ormore usage category records 650. A usage record 680 may storeinformation relating to behavior patterns associated with a particularusage category. Usage category record 680 may include a usage categoryID field 682 and one or more behavior pattern fields 684.

Usage category ID field 682 may store information identifying aparticular usage category. Each behavior pattern field 684 may identifyone or more behavior patterns associated with the particular usagecategory. Each behavior pattern may specify a sufficient condition foridentifying UE device 110 as being associated with the particular usagecategory. A sufficient condition may include one or more behaviorpatterns that must all be satisfied in order for UE device 110 to beidentified with the particular usage category. The particular usagecategory may be associated with multiple behavior pattern fields 684,each of which includes a sufficient condition. For example, a behaviorpattern may specify, for a time period of specified duration, an averagespeed of UE device 110, a maximum speed of UE device 110, an elevationrange of UE device 110, a number of cell changes in a time period for UEdevice 110, a maximum elevation of UE device 110, a number of bearers ofa particular Quality of Service (QoS) class associated with UE device110, a number of users associated with UE device 110, a data throughputfor a particular QoS class associated with UE device 110, a number ofhandovers associated with UE device 110, a packet size variabilityassociated with UE device 110, a variance in packet arrival times for UEdevice 110, a connection success rate for UE device 110, a call droprate for UE device 110, a latency associated with UE device 110, anumber of unique cells reported in a time period for UE device 110, anumber of cells changed in a time period for UE device 110, an errorrate associated with UE device 110, and/or another type of behaviorpattern associated with a metric value or metric range for UE device110.

Although FIG. 6C shows exemplary components of classification DB 450, inother implementations, classification DB 450 may include fewercomponents, different components, additional components, or differentlyarranged components than depicted in FIG. 6C.

FIG. 7 is a flowchart of a process for configuring a self-optimizingnetwork based on usage categories according to an implementationdescribed herein. In some implementations, the process of FIG. 7 may beperformed by SON system 150 and/or metrics system 160. In otherimplementations, some or all of the process of FIG. 7 may be performedby another device or a group of devices separate from SON system 150and/or metrics system 160, such as base station 130.

The process of FIG. 7 may include selecting a usage category (block 710)and determining classification patterns for the selected usage category(block 720). For example, a usage category may be designated as acombination of a data type, a movement type, and a user type. In someimplementations, all possible combinations of data type, movement type,and/or user type may be designation as different usage categories. Inother implementations, relevant combinations of data type, movementtype, and user type may be selected to designate a usage category basedon an estimated number of UE devices 110 using provider network 140 thatare likely to belong to a usage category.

UE devices 110 may be categorized to a particular usage category basedon a device ID. As an example, WiFi access points may be associated withparticular usage categories and when a particular UE device 110 isidentified as a WiFi access point, the usage category of the particularUE device 110 may be narrowed down to a subset of usage categories. Asanother example, particular applications may be associated withparticular usage categories. Thus, when a particular UE device 110 isdetected as using a particular application (e.g., based on anapplication ID included in a message sent by the particular UE device110), the particular UE device 110 may be associated with a usagecategory based on the particular application. As yet another example, UEdevices 110 may be configured to signal a device identifier whenevercommunicating via access network 120.

Furthermore, behavior patterns for usage categories may be determinedand stored in classification DB 450. Behavior patterns may be determinedbased on empirical observation, based on expected behavior of UE devices110 associated with a usage category, based on SON optimizationrequirements selected for a particular usage category, and/or based onanother criterion.

Thresholds for optimization actions for the selected usage category maybe set (block 730). For example, for each optimization action associatedwith a usage category, one or more metric values may be selected andassociated with threshold values. If a threshold value is exceeded, theoptimization action may be selected to be performed. Thresholds foroptimization restrictions for the selected usage category may be set(block 740). For example, a restriction may be associated with aparticular usage category that may restrict a particular optimizationaction from being performed if a threshold in a particular metric isreached.

Base station parameters may be optimized based on the usage categories,the optimization actions, and the optimization restrictions (block 750).For example, SON system 150 may use the determined usage categories, thedetermined behavior patterns, and the selected optimization actions andrestrictions to perform SON optimization actions on base stations 130,as described below with reference to FIG. 8.

FIG. 8 is a flowchart of a process for self-optimization based on usagecategories according to an implementation described herein. In someimplementations, the process of FIG. 8 may be performed by SON system150 and/or metrics system 160. In other implementations, some or all ofthe process of FIG. 8 may be performed by another device or a group ofdevices separate from SON system 150 and/or metrics system 160, such asbase station 130.

The process of FIG. 8 may include selecting a base station (block 810),obtaining device metrics for UE devices attached to the base station(block 820), and determine usage categories for the UE devices attachedto the selected base station (block 830). Metrics system 160 may collectmetrics, relating to attached UE devices 110, from base stations 130 atparticular intervals. UE devices 110 attached to base station 130 may beclassified into usage categories based on identifiers associated with UEdevices 110 and stored in UE device DB 440, based on behavior patternsassociated with UE devices and stored in classification DB 450, and/orbased on a combination of an identifier and a behavior patterns. Theidentifiers may correspond to device identifiers, communication protocolidentifiers, application identifiers, subscriber identifiers, and/orother types of identifiers. SON system 150 may select a particular basestation 130 included in the set of base stations managed by SON 150, andmay retrieve the classified metric values associated with the selectedbase station 130 from metrics DB 420.

The obtained device metrics may be classified based on the determinedusage categories (block 840) and optimization actions may be determinedfor each usage category based on the classified device metrics (block850). For example, metrics system 160 may classify the obtained metricvalues based on the determined usage categories associated with UEdevices 110 and may enable SON system 150 to access the classifiedmetric values stored in metrics DB 420. SON system 150 may determineusage categories associated with base station 130 and may accessoptimization DB 560 to determine optimization actions to be performedfor base station 130.

One or more optimization actions based on the usage categoriesassociated with the base station may be selected (block 860) and theselected base station may be instructed to perform the selected one ormore optimization actions (block 870). As an example, SON system 150 maycheck to see if a triggering event for an optimization action for aparticular usage category is satisfied in optimization DB 330 and tomake sure a restriction for the optimization action has not beenactivated. As another example, base station 130 may locally checkwhether a triggering event in optimization DB 560 has been satisfied fora particular usage category and to make sure a restriction for theoptimization action has not been activated. The selected base station130 may then be instructed to perform the selected one or moreoptimization actions.

In some situations, a conflict may exist between different usagecategories. For example, base station 130 may be associated withmultiple usage categories and different usage categories may indicatedifferent, and possibly conflicting optimization actions. In suchsituations, optimization DB 330 or 560 may specify priorities forparticular optimization actions and/or usage categories. As an example,usage categories associated with users may be given priority over usagecategories associated with MTC devices. As another example, a usagecategory associated with a higher number of UE devices 110 may be givenpriority over another usage category with a lower number of UE devices110. As yet another example, optimization actions to be performed may becomputed based on a weighted average of the usage categories associatedwith base station 130. Different situations may be associated withdifferent weights. For example, usage categories associated withvehicular speeds may be given higher weights during rush hours.

FIG. 9 is a diagram of an exemplary set 900 of behavior patterns fordifferent usage categories according to an implementation describedherein. The behavior patterns shown in FIG. 9 may be used to identify aparticular usage category in place of, or in addition to, a UE device ID(e.g., MAC address, IP address, MDN, etc.) associated with UE device110. As shown in FIG. 9, set 900 may include a voice usage category 910,a mobile broadband usage category 920, a mobile high speed usagecategory 930, a connected mass transit usage category 940, an airborneMTC device usage category 950, a video feed MTC device usage category960, a stationary low rate MTC device usage category 970, and a besteffort usage category 980.

Voice usage category 910 may correspond to UE device 110 with an activevoice QoS class bearer, indicating that user is using UE device 110 forvoice communication. Voice usage category 910 may be identified if thenumber of voice bearers in the last N attempts at setting up a bearer byUE device 110 is greater than zero. Mobile broadband usage category 920may correspond to UE device 110 with an active bearer that is not avoice bearer, but that is a higher QoS class than a best effort bearer(e.g., streaming video, real-time gaming, high priority data, etc.) andmay be identified if the number of non-voice high QoS bearers in thelast N attempts at setting up a bearer is greater than zero.

Mobile high speed usage category 930 may correspond to UE device 110moving at vehicular speeds, such as a user with a mobile phone in avehicle and may be identified if UE device 110 is associated with amaximum distance per change of time that is higher than a threshold T₁.Connected mass transit usage category 940 may correspond to UE device110 that is a WiFi access point in a mass transit vehicle, such as a busor a train, and may be identified if UE device 110 is associated with amaximum distance per change of time that is higher than a threshold T₂and if the number of different users associated with UE device 110 ishigher than a threshold T₃. The number of different users may beidentified by, for example, the number of different IP addressesassociated with UE device 110, the number of different bearersassociated with UE device 110, the number of different activeapplication sessions associated with UE device 110, and/or based on adifferent technique.

Airborne MTC device usage category 950 may correspond a UE device 110that is an unmanned aerial vehicle, such as a drone. Airborne MTC deviceusage category 950 may be identified if the number of measurementreports (sent to determine whether a handover should occur) per unittime is greater than a threshold T₄, or the number of unique cells in ameasurement report is greater than a threshold T₅, or the maximumelevation is greater than a threshold T₆, or the change in elevation(measured as the difference between a maximum and a minimum elevation)is greater than a threshold T₇. Thus, in this example, four differentbehavior patterns may be used to identify UE device 110 as associatedwith an airborne MTC device usage category 950.

Video feed MTC device usage category 960 may correspond to an MTC devicethat provides a wireless signal feed of video signals captured by avideo camera. Video feed MTC device usage category 960 may be identifiedif the number of voice bearers in the last N attempts at establishing abearer are zero, and if the packet size variability is less than orequal to a threshold T₈, and if the variance in packet arrival times isless than a threshold T₉. Thus, in this example, three differentbehavior patterns must be satisfied to identify UE device 110 asassociated with a video feed MTC device usage category 960.

Stationary low rate MTC device usage category 970 may correspond to alow data rate stationary MTC device, such as a parking meter, a utilitymeter, a road sensor, a smart street light, etc. Stationary low rate MTCdevice usage category 970 may be identified if the number of handoversper unit time is less than a threshold T₁₀, and if the maximum distancechange per unit time is less than a threshold T₁₁, and if the maximumnumber of bytes sent per attach request is less than a threshold T₁₂.Thus, in this example, three different behavior patterns must besatisfied to identify UE device 110 as associated with a stationary lowrate MTC device usage category 970. Best effort usage category 980 maycorrespond to a device that only sends and receives data identified asbest effort QoS class. Best effort usage category 980 may be identifiedif the number of voice and other high QoS bearers in the last N attemptscorresponds to zero.

FIG. 10 is a diagram of an exemplary set 1000 of optimization actionsfor different usage categories according to an implementation describedherein. As shown in FIG. 10, set 1000 may include a voice usage categoryoptimization action set 1010, a mobile broadband usage categoryoptimization action set 1020, a mobile high speed usage categoryoptimization action set 1030, a connected mass transit usage categoryoptimization action set 1040, an airborne MTC device usage categoryoptimization action set 1050, a video feed MTC device usage categoryoptimization action set 1060, a stationary low rate MTC device usagecategory optimization action set 1070, and a best effort usage categoryoptimization action set 1080.

Voice usage category optimization action set 1010 may include coverageoptimization. Coverage optimization may include adjusting the RFparameters (e.g., transmit power, antenna azimuth, antenna beam width,downlink or uplink band selection, etc.) of one or more adjacent cellsof base station 130 to optimize coverage in a particular area.Additionally or alternatively, coverage optimization may be coordinatedbetween cells of adjacent base stations 130.

Mobile broadband usage category optimization action set 1020 may includeremote electric tilt adjustments, neighbor list changes, and handoverparameter changes. Thus, if base station 130 is associated with a mobilebroadband usage category, adjustments to antenna remote electric tiltmay be permitted and performed when a determination is made that theremote electric tilt is to be adjusted to optimize performance.

Mobile high speed usage category optimization action set 1030 mayinclude handover parameter changes. Thus, if base station 130 isassociated with a mobile high speed usage category, handover parameterchanges may be permitted and performed when a determination is made thatperformance would be improved by adjusting the handover parameters.

Connected mass transit usage category optimization action set 1040 mayinclude handover parameter changed and capacity optimization. Capacityoptimization may include load balancing of traffic of particular QoSclasses across multiple cells and/or across multiple base stations 130.Thus, if base station 130 is associated with a connected mass transitusage category, handover parameter changes and capacity optimization maybe permitted and performed when a determination is made that performancewould be improved by adjusting the handover parameters or by adjustingthe capacity of base station 130.

Airborne MTC device usage category optimization action set 1050 mayinclude antenna auxiliary tilt and secondary carrier optimization.Antenna auxiliary tilt adjustment and/or changing a secondary componentcarrier in a carrier aggregation scheme may improve coverage at higherelevation. Thus, if base station 130 is associated with an airborne MTCdevice usage category, antenna auxiliary tilt and secondary carrieroptimization may be permitted and performed when a determination is madethat performance would be improved by adjusting the antenna auxiliarybeam tilt or changing the secondary component carrier.

Video feed MTC device usage category optimization action set 1060 mayinclude capacity optimization. Thus, if base station 130 is associatedwith a video feed MTC usage category, capacity optimization may bepermitted and performed when a determination is made that performancewould be improved by adjusting the capacity of base station 130.

Stationary low rate MTC device usage category optimization action set1070 may include physical random access channel (PRACH) optimization anddelay optimization. A PRACH may be used for initiation of a randomaccess procedure, which is used to initiate a data transfer. Stationarylow rate MTC devices may utilize a PRACH to initiate a data transfer.PRACH optimization may include optimizing parameters associated with aPRACH, such as power control parameters (e.g., initial transmissionpower, change in transmit power on subsequent attempts, number ofattempts, etc.) for a PRACH. Delay optimization may be associated with adiscontinuous reception (DRX) mode used by stationary low rate MTCdevices. A UE device 110 in DRX mode may be in a power saving mode andmay need to wake up before communication with base station 130. Thus, atradeoff may exist before delay and power saving. Delay optimization mayinclude adjusting DRX parameters (e.g., DRX inactivity timer, DRX cycletimer, DRX retransmission timer, etc.) to balance power saving benefitswith communication delays. Thus, if base station 130 is associated witha stationary low rate MTC usage category, PRACH and delay optimizationmay be permitted and performed when a determination is made thatperformance would be improved by adjusting the PRACH or delayparameters. Best effort usage category optimization action set 1080 maybe empty, as SON system 150 may determine that best effort trafficshould not drive optimization

While FIGS. 9 and 10 depict a set of usage categories for illustrationpurposes, in practice, the set of usage categories may include fewerusage categories, different usage categories, or additional usagecategories. For example, any combination of data type, movement type,and user type may be designated as a usage category. Other examples ofusage categories include a stationary WiFi access point usage category,an airborne WiFi usage category, a pedestrian speed MTC device usagecategory, a vehicular speed MTC usage category, a vehicular speed videofeed MTC usage category, a pedestrian speed critical data MTC device(e.g., a wearable health monitoring device) usage category, etc.

In the preceding specification, various preferred embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

For example, while a series of blocks have been described with respectto FIGS. 7 and 8, the order of the blocks may be modified in otherimplementations. Further, non-dependent blocks may be performed inparallel.

It will be apparent that systems and/or methods, as described above, maybe implemented in many different forms of software, firmware, andhardware in the implementations illustrated in the figures. The actualsoftware code or specialized control hardware used to implement thesesystems and methods is not limiting of the embodiments. Thus, theoperation and behavior of the systems and methods were described withoutreference to the specific software code—it being understood thatsoftware and control hardware can be designed to implement the systemsand methods based on the description herein.

Further, certain portions, described above, may be implemented as acomponent that performs one or more functions. A component, as usedherein, may include hardware, such as a processor, an ASIC, or a FPGA,or a combination of hardware and software (e.g., a processor executingsoftware).

It should be emphasized that the terms “comprises”/“comprising” whenused in this specification are taken to specify the presence of statedfeatures, integers, steps or components but does not preclude thepresence or addition of one or more other features, integers, steps,components or groups thereof.

The term “logic,” as used herein, may refer to a combination of one ormore processors configured to execute instructions stored in one or morememory devices, may refer to hardwired circuitry, and/or may refer to acombination thereof. Furthermore, a logic may be included in a singledevice or may be distributed across multiple, and possibly remote,devices.

For the purposes of describing and defining the present invention, it isadditionally noted that the term “substantially” is utilized herein torepresent the inherent degree of uncertainty that may be attributed toany quantitative comparison, value, measurement, or otherrepresentation. The term “substantially” is also utilized herein torepresent the degree by which a quantitative representation may varyfrom a stated reference without resulting in a change in the basicfunction of the subject matter at issue.

To the extent the aforementioned embodiments collect, store or employpersonal information provided by individuals, it should be understoodthat such information shall be used in accordance with all applicablelaws concerning protection of personal information. Additionally, thecollection, storage and use of such information may be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as may be appropriate for thesituation and type of information. Storage and use of personalinformation may be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

No element, act, or instruction used in the present application shouldbe construed as critical or essential to the embodiments unlessexplicitly described as such. Also, as used herein, the article “a” isintended to include one or more items. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise.

What is claimed is:
 1. A method comprising: selecting, by a computerdevice, a base station; obtaining, by the computer device, one or moremetric values for user equipment (UE) devices attached to the selectedbase station; determining, by the computer device, usage categories forone or more of the UE devices attached to the selected base station,wherein a usage category identifies a particular combination of at leasttwo of a data type, a movement type, or a user type associated with aparticular UE device; classifying, by the computer device, the obtainedone or more metric values based on the determined usage categories;selecting, by the computer device, one or more optimization actions forthe selected base station based on the classified one or more metricvalues; and instructing, by the computer device, the selected basestation to perform the selected one or more optimization actions.
 2. Themethod of claim 1, wherein determining the usage categories for one ormore of the UE devices attached to the selected base station includes:selecting a UE device attached to the selected base station; obtaining adevice identifier for the selected UE device; and identifying a usagecategory for the selected UE device based on the obtained deviceidentifier.
 3. The method of claim 1, wherein determining the usagecategories for one or more of the UE devices attached to the selectedbase station includes: selecting a UE device attached to the selectedbase station; determining one or more behavior patterns for the selectedUE device, wherein the one or more behavior patterns include at leastone of a performance metric value, a connectivity metric value, aservice type metric value, or a location or movement metric value; andwherein determining a usage category for the selected UE device is basedon the determined one or more behavior patterns.
 4. The method of claim1, wherein determining the usage categories for one or more of the UEdevices attached to the selected base station is based on determiningtwo or more metric values for a UE device attached to the selected basestation, wherein the two or more metric values include at least two ormore of: an elevation of the UE device; a maximum speed of the UEdevice; a number of voice bearers associated with the UE device; anumber of video bearers associated with the UE device; a number of usersassociated with the UE device; a data throughput for the UE device; anumber of handovers for the UE device; a packet size variability for theUE device; a variance in packet arrival times for the UE device; aconnection success rate for the UE device; a call drop rate for the UEdevice; a number of unique cells reported in a time period; a number ofcells changed in a time period; a latency for the UE device; or an errorrate for the UE device.
 5. The method of claim 1, wherein classifyingthe obtained one or more metric values based on the determined usagecategories includes classifying a UE device associated with a particularone of the obtained one or more metric values into: a voice usagecategory; a mobile broadband usage category; a mobile high speed usagecategory; a connected mass transit usage category; an airborneMachine-Type Communication (MTC) usage category; a video feed MTC usagecategory; a stationary low data MTC device category; or a best effortusage category.
 6. The method of claim 1, wherein selecting the one ormore optimization actions for the selected base station based on theclassified one or more metric values includes selecting to adjust atleast one of: a coverage optimization parameter; a capacity optimizationparameter; a handover parameter; a neighbor list changes parameter; anantenna tilt parameter; a delay optimization parameter; a carrieroptimization parameter; or a random access channel parameter.
 7. Themethod of claim 1, wherein selecting the one or more optimizationactions for the selected base station based on the classified one ormore metric values includes: identifying a restriction associated withat least one of the determined usage categories, wherein the restrictiondesignates one or more parameters that are not to be adjusted; andselecting not to perform an optimization action for the designated oneor more parameters.
 8. The method of claim 1, wherein determining usagecategories for particular ones of the UE devices attached to theselected base station includes determining an airborne Machine-TypeCommunication (MTC) usage category for a UE device based on at least oneof: a number of measurement reports within a first time period for theUE device exceeding a first threshold, a number of unique base stationcells identified in a measurement report for the UE device exceeding asecond threshold, a number of cell changes in a time period, a maximumelevation for the UE device exceeding a third threshold, or a differencebetween maximum and minimum elevation within a second time period forthe UE device exceeding a fourth threshold; and wherein selecting theone or more optimization actions for the selected base station based onthe classified one or more metric values includes selecting the one ormore optimization actions based on the determined airborne MTC usagecategory, wherein the one or more optimization actions include at leastone of: optimizing an auxiliary beam tilt for an antenna associated withthe selected base station, or optimizing a second component carrierassociated with the selected base station.
 9. The method of claim 1,wherein determining usage categories for one or more of the UE devicesattached to the selected base station includes determining a stationarylow data rate Machine-Type Communication (MTC) usage category for a UEdevice based on at least one of: a number of handovers within a timeperiod for the UE device is zero, a maximum distance change within thetime period for the UE device is less than a first threshold, or amaximum number of bytes sent or received by the UE device per attachmentis less than a second threshold; and wherein selecting the one or moreoptimization actions for the selected base station based on theclassified one or more metric values includes selecting the one or moreoptimization actions based on the determined stationary low data rateMTC usage category, wherein the one or more optimization actions includeat least one of: optimizing a physical random access channel associatedwith the selected base station, or optimizing a communication delayassociated with the selected base station.
 10. The method of claim 1,wherein determining usage categories for one or more of the UE devicesattached to the selected base station includes determining a video feedMachine-Type Communication (MTC) usage category for a UE device based onat least one of: a number of video bearers set up within a time periodfor the UE device is zero, a packet size variability within the timeperiod for the UE device is less than a first threshold, or a variancein packet arrival times within the time period for the UE device is lessthan a second threshold; and wherein selecting the one or moreoptimization actions for the selected base station based on theclassified one or more metric values includes selecting the one or moreoptimization actions based on the determined video feed MTC usagecategory, wherein the one or more optimization actions include:performing a capacity optimization for the selected base station. 11.The method of claim 1, wherein determining usage categories for one ormore of the UE devices attached to the selected base station includesdetermining a connected mass transit usage category for a UE devicebased on determining that the UE device is associated with a number ofusers that is greater than a user threshold; and wherein selecting theone or more optimization actions for the selected base station based onthe classified one or more metric values includes selecting the one ormore optimization actions based on the determined connected mass transitcategory, wherein the one or more optimization actions include at leastone of: optimizing handover parameters associated with the selected basestation, or performing a capacity optimization for the selected basestation.
 12. The method of claim 1, further comprising: selecting ausage category; selecting an optimization action associated with theusage category; and setting an optimization threshold for a metric valueassociated with the selected optimization action, wherein theoptimization action is to be performed with respect to the selectedusage category when the optimization threshold for the metric value isexceeded.
 13. A computer device comprising: a memory configured to storeinstructions; and a processor configured to execute the instructions to:select a base station; obtain one or more metric values for userequipment (UE) devices attached to the selected base station; determineusage categories for particular ones of the UE devices attached to theselected base station, wherein a usage category identifies a particularcombination of at least two of a data type, a movement type, or a usertype associated with a particular UE device; classify the obtained oneor more metric values based on the determined usage categories; selectone or more optimization actions for the selected base station based onthe classified one or more metric values; and instruct the selected basestation to perform the selected one or more optimization actions. 14.The computer device of claim 13, wherein, when determining the usagecategories for particular ones of the UE devices attached to theselected base station, the processor is further configured to executethe instructions to: select a UE device attached to the selected basestation; obtain a device identifier for the selected UE device; andidentify a usage category for the selected UE device based on theobtained device identifier.
 15. The computer device of claim 13,wherein, when determining the usage categories for particular ones ofthe UE devices attached to the selected base station, the processor isfurther configured to execute the instructions to: select a UE deviceattached to the selected base station; determine one or more behaviorpatterns for the selected UE device, wherein the one or more behaviorpatterns include at least one of a performance metric value, aconnectivity metric value, a service type metric value, or a location ormovement metric value; and identify a usage category for the selected UEdevice based on the determined one or more behavior patterns.
 16. Thecomputer device of claim 13, wherein determining the usage categoriesfor particular ones of the UE devices attached to the selected basestation is based on determining two or more metric value for a UE deviceattached to the selected base station, wherein the two or more metricvalues include at least two of: an elevation of the UE device; a maximumspeed of the UE device; a number of voice bearers associated with the UEdevice; a number of video bearers associated with the UE device; anumber of users associated with the UE device; a data throughput for theUE device; a number of handovers for the UE device; a packet sizevariability for the UE device; a variance in packet arrival times forthe UE device; a connection success rate for the UE device; a call droprate for the UE device; a latency for the UE device; or an error ratefor the UE device.
 17. The computer device of claim 13, wherein, whenclassifying the obtained one or more metric values based on thedetermined usage categories, the processor is further configured toexecute the instructions to classify a UE device associated with aparticular one of the obtained one or more metric values into: a voiceusage category; a mobile broadband usage category; a mobile high speedusage category; a connected mass transit usage category; an airborneMachine-Type Communication (MTC) usage category; a video feed MTC usagecategory; a stationary low data MTC device category; or a best effortusage category.
 18. The computer device of claim 13, wherein, whenselecting the one or more optimization actions for the selected basestation based on the classified one or more metric values, the processoris further configured to execute the instructions to select to adjust atleast one of: a coverage optimization parameter; a capacity optimizationparameter; a handover parameter; a neighbor list changes parameter; anantenna tilt parameter; a delay optimization parameter; a carrieroptimization parameter; or a random access channel parameter.
 19. Thecomputer device of claim 13, wherein selecting the one or moreoptimization actions for the selected base station based on theclassified one or more metric values includes: identifying a restrictionassociated with at least one of the determined usage categories, whereinthe restriction designates one or more parameters that are not to beadjusted; and selecting not to perform an optimization action for thedesignated one or more parameters.
 20. A system comprising: a basestation configured to provide a wireless connection to a wirelesscommunication device; and a network optimizing device configured to:obtain one or more metric values for user equipment (UE) devicesattached to the base station; determine usage categories for particularones of the UE devices attached to the base station, wherein a usagecategory identifies a particular combination of at least two of a datatype, a movement type, or a user type associated with a particular UEdevice; classify the obtained one or more metric values based on thedetermined usage categories; select one or more optimization actions forthe base station based on the classified one or more metric values; andinstruct the base station to perform the selected one or moreoptimization actions.